Machine learning

Machine learning applications are already ubiquitous. How does it work? This videographic answers. VIDEOGRAPHIC

Transcript

00:00Machine learning applications are ubiquitous. We are using them on a daily basis, often without realizing it.

00:16On your smartphone alone, there are already countless applications.

00:19When you log on, browse for products while shopping online, check emails, type a text message, or plan a route.

00:26So what is machine learning and how does it work under the hood?

00:29It is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed,

00:35by enabling them to learn from data and experience.

00:39Sometimes an algorithm can learn a task under supervision.

00:42This involves using labelled data to train a model to make predictions or decisions.

00:47It is used on online shopping to predict a user's preferences based on historical data such as purchase history,

00:54browsing behavior, and product attributes, and train a model to recommend products accordingly.

00:59Other times algorithms can learn without being supervised.

01:03This involves identifying patterns and relationships in unstructured data to create structure and categorization.

01:10It is used to cluster different products based on attributes such as price, category, brand, and descriptions,

01:18without labelled examples or guidance.

01:21The goal is to find hidden structures and patterns and identify similarities or differences between products.

01:27Finally, sometimes algorithms learn from their own performance and previous experiences,

01:32as they adjust their behavior to improve the decision-making process.

01:36This can be used to train a model to recommend products that maximize user engagement or satisfaction,

01:41by recommending the right products to the right users.

01:44Machine learning has a wide array of applications, but it comes with ethical implications we need to consider,

01:50surrounding issues related to bias, fairness, accountability, privacy, and transparency.